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The Ranking Prediction of NBA Playoffs Based on Improved Pagerank Algorithm Hindawi Complexity Volume 2021, Article ID 6641242, 10 pages https://doi.org/10.1155/2021/6641242 Research Article The Ranking Prediction of NBA Playoffs Based on Improved PageRank Algorithm Fan Yang 1 and Jun Zhang2 1School of Management, Xi’an Polytechnic University, Xi’an 710048, Shaanxi, China 2Xi’an Wannian Technology Industry Co., Ltd., Xi’an 710038, Shaanxi, China Correspondence should be addressed to Fan Yang; [email protected] Received 23 December 2020; Revised 27 January 2021; Accepted 3 February 2021; Published 16 February 2021 Academic Editor: Wei Wang Copyright © 2021 Fan Yang and Jun Zhang. *is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. It is of great significance to predict the results accurately based on the statistics of sports competition for participants research, commercial cooperation, advertising, and gambling profit. Aiming at the phenomenon that the PageRank page sorting algorithm is prone to subject deviation, the category similarity between pages is introduced into the PageRank algorithm. In the PR value calculation formula of the PageRank algorithm, the factor W(u, v) between pages is added to replace the original Nu (the number of links to page u). In this way, the content category between pages is considered, and the shortcoming of theme deviation will be improved. *e time feedback factor in the PageRank-time algorithm is used for reference, and the time feedback factor is added to the first improved PR value calculation formula. Based on statistics from 1230 games during the NBA 2018-2019 regular season, this paper ranks the team strength with improved PageRank algorithm and compares the results with the ranking of regular- season points and the result of playoffs. *e results show that it is consistent with the regular-season points ranking in the eastern division by the use of improved PageRank algorithm, but there is a difference in the second ranking in the western division. In the prediction of top four in playoffs, it predicts three of the four teams. 1. Introduction knowledge of hyperlinks on web pages. *e basic idea of PageRank algorithm can be understood in this way. First, the *ere are many factors involved in the results of competitive PageRank algorithm evaluates whether a web page is im- games, and many factors need to be considered when fore- portant, based on the number of webpages linked to this web casting. *e prediction of competitive competitions in team page. We all know that the importance of the Phoenix.com battles is more complicated. In addition to personal abilities homepage is higher than that of a personal blog page, but the and personal on-the-spot performance, the factors involved specific importance is measured by the number of web pages in the results of the competition also include cooperative linked to these two web pages. Specifically, the number of combat capabilities such as team cooperation. *erefore, the web pages linked to the homepage of Phoenix.com is more prediction of the outcome of the game is a very professional than the number of pages linked to a personal blog. field problem. *e NBA’s data system is amazing to the degree *erefore, the homepage of Phoenix.com is more important. of quantification of the game. *e NBA has always relied on However, in order to improve the importance of some cutting-edge technology for support, while providing a large webpages, in addition to improving the quality of their own amount of data for game prediction and game analysis. *e web page content, they will also create some webpages strength gap between each team is small, and each game is full linking themselves, and many of them are even spam of infinite possibilities. *is makes predicting the game a webpages. Although the index of importance has increased, challenging and meaningful thing. these pages are not important pages. In order to avoid the PageRank algorithm is an algorithm based on link drawbacks of evaluating the importance of webpages by analysis. *e principle of the algorithm involves the linking, the PageRank algorithm uses a method of weighting 2 Complexity the importance of linked webpages for assessment. For based on the vector space model theory, which repre- example, if a web page linked to a web page contains some sented both user queries and web pages as vectors [20–22]. webpages from well-known websites such as Google, the *e difference between the Hits algorithm and PageRank importance of this page is even higher. is that certain web pages are identified as Authority pages and It is significantly meaningful to evaluate the strength of Hub pages in the Hits algorithm. *e traditional PageRank the competitors and predict the results of the competition algorithm is calculated based on web page hyperlinks, but the according to the strength. Zak et al. (1979) calculated the value of each web page link cannot be used to measure its offensive strength and defensive strength of each team based importance and can only be calculated by using the average on the statistical analysis of the technical characteristics of value. *e Hits algorithm solves this problem well. *e Hits NBA games, so as to rank the comprehensive strength of algorithm is one of the very classic algorithms in link analysis. teams and predict the results of the game [1]. Wu established *e current search engine Teoma uses the Hits algorithm as a the principal component logistic regression model to predict link analysis algorithm. After the Hits algorithm receives the the victory or defeat of the match based on the data of the user’s query, it submits the query to an existing search engine first 30 matches in the 2010-2011 season of Italian Football (or a search system constructed by itself) and extracts the top League A [2]. Since the end of the 20th century, a large web pages from the returned search results to obtain a set of number of researchers began to use Machine Learning queries related to the user collection of highly related initial Algorithm to predict the results. Cuzzocrea et al. combined web pages. *is collection is called the Root Set. On the basis the deep-learning and transfer-learning approach for sup- of the root set, the Hits algorithm expands the set of web porting social influence prediction [3]. Huang et al. and Liu pages. All web pages that have a direct link to the web pages in and Zhu predicted different target domains based on Pag- the root set will be expanded, and it is expanded into the eRank and HITS algorithm [4, 5]. Goel et al. and Liu et al., extended page collection. *e Hits algorithm searches for a respectively, proposed sNorm(p) algorithm and HITS-PR- good “Hub” page and a good “Authority” page in this ex- HHblits algorithm to further improve the predictive per- panded web page collection. When the PageRank algorithm formance [6, 7]. calculates the relevance ranking, only one PageRank value is For the research of sorting algorithm, foreign coun- obtained, while when the Hits algorithm calculates, each page tries are earlier than domestic. PageRank algorithm and will generate two scores, namely, the Authority weight and HITS algorithm are two representative sorting algorithms the Hub score. *e former is very useful in the search engine [8–10]. PageRank is a link analysis algorithm, which is also field. a calculation model that other search engines and aca- Generally speaking, the commonly used evaluation demia pay close attention to. *e core idea is that the more methods of competitive games belong to the evaluation the links a web page has, the more authoritative is the web model of multiparameter input and single result output. page that references it and the more important the web Although it can reflect the strong weak relationship of in- page is. *e calculation of the importance of web pages is dividual matches, it cannot reflect the overall characteristics carried out offline and has nothing to do with the subject and interaction of the whole data group. However, these of the query, so it has fast response capabilities [11]. factors are the key basis for determining the ranking of However, it also has obvious shortcomings such as subject teams. In order to solve the shortcomings of the above- drift, discrimination against new web pages, and ignoring mentioned research methods, this paper constructs a new the individual needs of users. *e HITS algorithm uses PageRank algorithm based on the weight transfer between two mutually influential weights, content authority and research objects. *en, it is applied to the NBA game re- link authority, to evaluate the value of web content and the search, and the prediction results are compared with the value of hyperlinks in the web [12–14]. It is related to the previous points ranking data. *e results show that the query subject. *e interdependent and mutually rein- method is effective for predicting the results of competitive forcing relationship between Authority and Hub is the competitions. basis of the HITS algorithm [15]. *e algorithm also has the problems of subject drift, low computational effi- ciency, unstable structure, and easy deception. Relevant 2. Method Description scholars first use the vector space model VSM to calculate *is paper attempts to apply the method of ranking the the similarity weights between web pages, then analyze importance of Google search engine pages to the prediction and count the incremental weights of web page clicks, and of NBA team playoff results. Firstly, the weight transfer finally, combine the two weights to integrate feedback matrix is constructed by using the score relationship be- information and content relevance to improve the Pag- tween teams, and then, iterative calculation is carried out eRank algorithm and improve the relevance of search according to the improved PageRank matrix.
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